SYSTEMS AND METHODS FOR ARBITRARY LEVEL CONTRAST DOSE SIMULATION IN MRI
Methods and systems are provided for simulating images with different dosages. The method comprises: learning a mapping relationship from a post-contrast image to a low-dose image using an iterative method, where learning the mapping comprises generating a plurality of images with intermediate dosages; and applying the mapping relationship to an input images with a higher dose level and a lower dose level to generate one or more simulated images with intermediate dose levels between the higher dose level and the lower dose level.
1 . A computer-implemented method for simulating images with different contrast enhancement levels, the computer-implemented method comprising:
providing an iterative model comprising a plurality of iterations, wherein a given iteration comprises a deep learning model configured to i) take an input comprising a synthesized image generated by a previous iteration, wherein the synthesized image has a first intermediate contrast enhancement level, and ii) output a corresponding synthesized image has a second intermediate contrast enhancement level, wherein the second intermediate contrast enhancement level is lower than the first intermediate contrast enhancement level; and
applying the iterative model to a first input image corresponding to a higher contrast enhancement level and a second input image corresponding to a lower contrast enhancement level, and outputting a plurality of synthesized images corresponding to a plurality of intermediate contrast enhancement levels between the higher contrast enhancement level and the lower contrast enhancement.
2 . The computer-implemented method of claim 1 , wherein the deep learning model comprises a transformer model.
3 . The computer-implemented method of claim 2 , wherein the deep learning model comprises a sequence of global transformer blocks.
4 . The computer-implemented method of claim 3 , wherein at least one of the global transformer blocks comprises a subsample process to generate a sub-image as an attention feature map.
5 . The computer-implemented method of claim 4 , wherein the sub-image is sampled at a stride to extract global information from the image data.
6 . The computer-implemented method of claim 1 , wherein the deep learning model in each iteration is trained based at least in part on a simulated truth image.
7 . The computer-implemented method of claim 1 , wherein the iterative model or the deep learning model is trained utilizing a training dataset comprising a pre-contrast image, a post-contrast image and a low-dose image.
8 . The computer-implemented method of claim 1 , wherein the iterative model or the deep learning model is trained utilizing a training dataset comprising a first image corresponding to a first contrast dose level, a second image corresponding to a second contrast dose level and a third image corresponding to a third contrast dose level, wherein the first contrast dose level is higher than the second contrast dose level which is higher than the third contrast dose level.
9 . The computer-implemented method of claim 8 , wherein the second image is used as ground truth for the training.
10 . The computer-implemented method of claim 1 , wherein the first input image or the second input image is acquired by a transforming magnetic resonance (MR) device.
11 . A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
providing an iterative model comprising a plurality of iterations, wherein a given iteration comprises a deep learning model configured to i) take an input comprising a synthesized image generated by a previous iteration, wherein the synthesized image has a first intermediate contrast enhancement level, and ii) output a corresponding synthesized image has a second intermediate contrast enhancement level, wherein the second intermediate contrast enhancement level is lower than the first intermediate contrast enhancement level; and
applying the iterative model to a first input image corresponding to a higher contrast enhancement level and a second input image corresponding to a lower contrast enhancement level, and outputting a plurality of synthesized images corresponding to a plurality of intermediate contrast enhancement levels between the higher contrast enhancement level and the lower contrast enhancement.
12 . The non-transitory computer-readable storage medium of claim 11 , wherein the deep learning model comprises a transformer model.
13 . The non-transitory computer-readable storage medium of claim 12 , wherein the deep learning model comprises a sequence of global transformer blocks.
14 . The non-transitory computer-readable storage medium of claim 13 , wherein at least one of the global transformer blocks comprises a subsample process to generate a sub-image as an attention feature map.
15 . The non-transitory computer-readable storage medium of claim 14 , wherein the sub-image is sampled at a stride to extract global information from the image data.
16 . The non-transitory computer-readable storage medium of claim 11 , wherein the deep learning model in each iteration is trained based at least in part on a simulated truth image.
17 . The non-transitory computer-readable storage medium of claim 11 , wherein the iterative model or the deep learning model is trained utilizing a training dataset comprising a pre-contrast image, a post-contrast image and a low-dose image.
18 . The non-transitory computer-readable storage medium of claim 11 , wherein the iterative model or the deep learning model is trained utilizing a training dataset comprising a first image corresponding to a first contrast dose level, a second image corresponding to a second contrast dose level and a third image corresponding to a third contrast dose level, wherein the first contrast dose level is higher than the second contrast dose level which is higher than the third contrast dose level.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein the second image is used as ground truth for the training
20 . The non-transitory computer-readable storage medium of claim 11 , wherein the first input image or the second input image is acquired by a transforming magnetic resonance (MR) device.